{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                satisfaction_level  last_evaluation  average_montly_hours  \\\n",
      "number_project                                                              \n",
      "2                             2388             2388                  2388   \n",
      "3                             4055             4055                  4055   \n",
      "4                             4365             4365                  4365   \n",
      "5                             2761             2761                  2761   \n",
      "6                             1174             1174                  1174   \n",
      "7                              256              256                   256   \n",
      "\n",
      "                time_spend_company  Work_accident  left  \\\n",
      "number_project                                            \n",
      "2                             2388           2388  2388   \n",
      "3                             4055           4055  4055   \n",
      "4                             4365           4365  4365   \n",
      "5                             2761           2761  2761   \n",
      "6                             1174           1174  1174   \n",
      "7                              256            256   256   \n",
      "\n",
      "                promotion_last_5years  Departments  salary  \n",
      "number_project                                              \n",
      "2                                2388         2388    2388  \n",
      "3                                4055         4055    4055  \n",
      "4                                4365         4365    4365  \n",
      "5                                2761         2761    2761  \n",
      "6                                1174         1174    1174  \n",
      "7                                 256          256     256  \n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('employee_data.csv')\n",
    "number_projects = df.groupby('number_project').count()\n",
    "print(number_projects)\n",
    "plt.bar(number_projects.index.values, number_projects['satisfaction_level'])\n",
    "plt.xlabel('number of projects')\n",
    "plt.ylabel('employee number')\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='Departments'>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Departments').count()['left'].plot(kind='bar')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}